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GLM-5.1 - state-of-the-art agentic engineering, now available on DeepInfra!
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Kimi-K2.6
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Kimi K2.6 is an open-source, native multimodal agentic model that advances practical capabilities in long-horizon coding, coding-driven design, proactive autonomous execution, and swarm-based task orchestration.
Kimi-K2.6
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Model Information
1. Model Introduction
Kimi K2.6 is an open-source, native multimodal agentic model that advances practical capabilities in long-horizon coding, coding-driven design, proactive autonomous execution, and swarm-based task orchestration.
Key Features
- Long-Horizon Coding: K2.6 achieves significant improvements on complex, end-to-end coding tasks, generalizing robustly across programming languages (Rust, Go, Python) and domains spanning front-end, DevOps, and performance optimization.
- Coding-Driven Design: K2.6 is capable of transforming simple prompts and visual inputs into production-ready interfaces and lightweight full-stack workflows, generating structured layouts, interactive elements, and rich animations with deliberate aesthetic precision.
- Elevated Agent Swarm: Scaling horizontally to 300 sub-agents executing 4,000 coordinated steps, K2.6 can dynamically decompose tasks into parallel, domain-specialized subtasks, delivering end-to-end outputs from documents to websites to spreadsheets in a single autonomous run.
- Proactive & Open Orchestration: For autonomous tasks, K2.6 demonstrates strong performance in powering persistent, 24/7 background agents that proactively manage schedules, execute code, and orchestrate cross-platform operations without human oversight.
2. Model Summary
| Architecture | Mixture-of-Experts (MoE) |
| Total Parameters | 1T |
| Activated Parameters | 32B |
| Number of Layers (Dense layer included) | 61 |
| Number of Dense Layers | 1 |
| Attention Hidden Dimension | 7168 |
| MoE Hidden Dimension (per Expert) | 2048 |
| Number of Attention Heads | 64 |
| Number of Experts | 384 |
| Selected Experts per Token | 8 |
| Number of Shared Experts | 1 |
| Vocabulary Size | 160K |
| Context Length | 256K |
| Attention Mechanism | MLA |
| Activation Function | SwiGLU |
| Vision Encoder | MoonViT |
| Parameters of Vision Encoder | 400M |
3. Evaluation Results
| Benchmark | Kimi K2.6 | GPT-5.4 (xhigh) | Claude Opus 4.6 (max effort) | Gemini 3.1 Pro (thinking high) | Kimi K2.5 |
|---|---|---|---|---|---|
| Agentic | |||||
| HLE-Full (w/ tools) | 54.0 | 52.1 | 53.0 | 51.4 | 50.2 |
| BrowseComp | 83.2 | 82.7 | 83.7 | 85.9 | 74.9 |
| BrowseComp (Agent Swarm) | 86.3 | 78.4 | |||
| DeepSearchQA (f1-score) | 92.5 | 78.6 | 91.3 | 81.9 | 89.0 |
| DeepSearchQA (accuracy) | 83.0 | 63.7 | 80.6 | 60.2 | 77.1 |
| WideSearch (item-f1) | 80.8 | - | - | - | 72.7 |
| Toolathlon | 50.0 | 54.6 | 47.2 | 48.8 | 27.8 |
| MCPMark | 55.9 | 62.5* | 56.7* | 55.9* | 29.5 |
| Claw Eval (pass^3) | 62.3 | 60.3 | 70.4 | 57.8 | 52.3 |
| Claw Eval (pass@3) | 80.9 | 78.4 | 82.4 | 82.9 | 75.4 |
| APEX-Agents | 27.9 | 33.3 | 33.0 | 32.0 | 11.5 |
| OSWorld-Verified | 73.1 | 75.0 | 72.7 | - | 63.3 |
| Coding | |||||
| Terminal-Bench 2.0 (Terminus-2) | 66.7 | 65.4* | 65.4 | 68.5 | 50.8 |
| SWE-Bench Pro | 58.6 | 57.7 | 53.4 | 54.2 | 50.7 |
| SWE-Bench Multilingual | 76.7 | - | 77.8 | 76.9* | 73.0 |
| SWE-Bench Verified | 80.2 | - | 80.8 | 80.6 | 76.8 |
| SciCode | 52.2 | 56.6 | 51.9 | 58.9 | 48.7 |
| OJBench (python) | 60.6 | - | 60.3 | 70.7 | 54.7 |
| LiveCodeBench (v6) | 89.6 | - | 88.8 | 91.7 | 85.0 |
| Reasoning & Knowledge | |||||
| HLE-Full | 34.7 | 39.8 | 40.0 | 44.4 | 30.1 |
| AIME 2026 | 96.4 | 99.2 | 96.7 | 98.3 | 95.8 |
| HMMT 2026 (Feb) | 92.7 | 97.7 | 96.2 | 94.7 | 87.1 |
| IMO-AnswerBench | 86.0 | 91.4 | 75.3 | 91.0* | 81.8 |
| GPQA-Diamond | 90.5 | 92.8 | 91.3 | 94.3 | 87.6 |
| Vision | |||||
| MMMU-Pro | 79.4 | 81.2 | 73.9 | 83.0* | 78.5 |
| MMMU-Pro (w/ python) | 80.1 | 82.1 | 77.3 | 85.3* | 77.7 |
| CharXiv (RQ) | 80.4 | 82.8* | 69.1 | 80.2* | 77.5 |
| CharXiv (RQ) (w/ python) | 86.7 | 90.0* | 84.7 | 89.9* | 78.7 |
| MathVision | 87.4 | 92.0* | 71.2* | 89.8* | 84.2 |
| MathVision (w/ python) | 93.2 | 96.1* | 84.6* | 95.7* | 85.0 |
| BabyVision | 39.8 | 49.7 | 14.8 | 51.6 | 36.5 |
| BabyVision (w/ python) | 68.5 | 80.2* | 38.4* | 68.3* | 40.5 |
| V* (w/ python) | 96.9 | 98.4* | 86.4* | 96.9* | 86.9 |
Footnotes
- General Testing Details
- We report results for Kimi K2.6 and Kimi K2.5 with thinking mode enabled, Claude Opus 4.6 with max effort, GPT-5.4 with xhigh reasoning effort, and Gemini 3.1 Pro with a high thinking level.
- Unless otherwise specified, all Kimi K2.6 experiments were conducted with temperature = 1.0, top-p = 1.0, and a context length of 262,144 tokens.
- Benchmarks without publicly available scores were re-evaluated under the same conditions used for Kimi K2.6 and are marked with an asterisk (
*). Except where noted with an asterisk, all other results are cited from official reports.
- Reasoning Benchmarks
- IMO-AnswerBench scores for GPT-5.4 and Claude 4.6 were obtained from z.ai/blog/glm-5.1.
- Humanity's Last Exam (HLE) and other reasoning tasks were evaluated with a maximum generation length of 98,304 tokens. By default, we report results on the HLE full set. For the text-only subset, Kimi K2.6 achieves 36.4% accuracy without tools and 55.5% with tools.
- Tool-Augmented / Agentic Tasks
- Kimi K2.6 was equipped with search, code-interpreter, and web-browsing tools for HLE with tools, BrowseComp, DeepSearchQA, and WideSearch.
- For HLE-Full with tools, the maximum generation length is 262,144 tokens with a per-step limit of 49,152 tokens. We employ a simple context management strategy: once the context window exceeds the threshold, only the most recent round of tool-related messages is retained.
- For BrowseComp, we report scores obtained with context management using the same discard-all strategy as Kimi K2.5 and DeepSeek-V3.2.
- For DeepSearchQA, no context management was applied to Kimi K2.6 tests, and tasks exceeding the supported context length were directly counted as failed. Scores for Claude Opus 4.6, GPT-5.4, and Gemini 3.1 Pro on DeepSearchQA are cited from the Claude Opus 4.7 System Card.
- For WideSearch, we report results under the "hide tool result" context management setting. Once the context window exceeds the threshold, only the most recent round of tool-related messages is retained.
- The test system prompts are identical to those used in the Kimi K2.5 technical report.
- Claw Eval was conducted using version 1.1 with max-tokens-per-step = 16384.
- For APEX-Agents, we evaluate 452 tasks from the public 480-task release, as done by Artificial Analysis(excluding Investment Banking Worlds 244 and 246, which have external runtime dependencies)
- Coding Tasks
- Terminal-Bench 2.0 scores were obtained with the default agent framework (Terminus-2) and the provided JSON parser, operating in preserve thinking mode.
- For the SWE-Bench series of evaluations (including Verified, Multilingual, and Pro), we used an in-house evaluation framework adapted from SWE-agent. This framework includes a minimal set of tools—bash tool, createfile tool, insert tool, view tool, strreplace tool, and submit tool.
- All reported scores for coding tasks are averaged over 10 independent runs.
- Vision Benchmarks
- Max-tokens = 98,304, averaged over three runs (avg@3).
- Settings with Python tool use max-tokens-per-step = 65,536 and max-steps = 50 for multi-step reasoning.
- MMMU-Pro follows the official protocol, preserving input order and prepending images.
4. Native INT4 Quantization
Kimi-K2.6 adopts the same native int4 quantization method as Kimi-K2-Thinking.
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